Overview

Dataset statistics

Number of variables19
Number of observations11121
Missing cells0
Missing cells (%)0.0%
Duplicate rows380
Duplicate rows (%)3.4%
Total size in memory1.5 MiB
Average record size in memory145.0 B

Variable types

Unsupported3
Numeric12
Boolean1
Categorical3

Alerts

Dataset has 380 (3.4%) duplicate rowsDuplicates
acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
energy is highly overall correlated with acousticness and 1 other fieldsHigh correlation
explicit is highly overall correlated with track_genreHigh correlation
loudness is highly overall correlated with acousticness and 1 other fieldsHigh correlation
tempo is highly overall correlated with time_signatureHigh correlation
time_signature is highly overall correlated with tempoHigh correlation
track_genre is highly overall correlated with explicitHigh correlation
explicit is highly imbalanced (54.1%)Imbalance
time_signature is highly imbalanced (68.3%)Imbalance
artists is an unsupported type, check if it needs cleaning or further analysisUnsupported
album_name is an unsupported type, check if it needs cleaning or further analysisUnsupported
track_name is an unsupported type, check if it needs cleaning or further analysisUnsupported
popularity has 2151 (19.3%) zerosZeros
key has 1317 (11.8%) zerosZeros
instrumentalness has 4061 (36.5%) zerosZeros

Reproduction

Analysis started2024-03-13 09:38:03.539862
Analysis finished2024-03-13 09:38:50.641078
Duration47.1 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

artists
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size87.0 KiB

album_name
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size87.0 KiB

track_name
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size87.0 KiB

popularity
Real number (ℝ)

ZEROS 

Distinct97
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.852621
Minimum0
Maximum100
Zeros2151
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size87.0 KiB
2024-03-13T09:38:50.925024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115
median42
Q359
95-th percentile73
Maximum100
Range100
Interquartile range (IQR)44

Descriptive statistics

Standard deviation25.469493
Coefficient of variation (CV)0.69111755
Kurtosis-1.2515733
Mean36.852621
Median Absolute Deviation (MAD)20
Skewness-0.1855906
Sum409838
Variance648.69509
MonotonicityNot monotonic
2024-03-13T09:38:51.437199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2151
 
19.3%
1 226
 
2.0%
57 225
 
2.0%
59 221
 
2.0%
63 220
 
2.0%
60 210
 
1.9%
62 210
 
1.9%
58 203
 
1.8%
44 188
 
1.7%
61 187
 
1.7%
Other values (87) 7080
63.7%
ValueCountFrequency (%)
0 2151
19.3%
1 226
 
2.0%
2 91
 
0.8%
3 50
 
0.4%
4 33
 
0.3%
5 19
 
0.2%
6 5
 
< 0.1%
7 9
 
0.1%
8 3
 
< 0.1%
9 8
 
0.1%
ValueCountFrequency (%)
100 1
 
< 0.1%
98 1
 
< 0.1%
96 1
 
< 0.1%
93 1
 
< 0.1%
92 3
 
< 0.1%
91 1
 
< 0.1%
90 3
 
< 0.1%
89 5
< 0.1%
88 6
0.1%
87 9
0.1%

duration_ms
Real number (ℝ)

Distinct8264
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean229669.85
Minimum17826
Maximum4120258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size87.0 KiB
2024-03-13T09:38:51.916867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum17826
5-th percentile117468
Q1173947
median213520
Q3264210
95-th percentile376640
Maximum4120258
Range4102432
Interquartile range (IQR)90263

Descriptive statistics

Standard deviation120182.6
Coefficient of variation (CV)0.52328418
Kurtosis256.84214
Mean229669.85
Median Absolute Deviation (MAD)43186
Skewness11.007197
Sum2.5541584 × 109
Variance1.4443858 × 1010
MonotonicityNot monotonic
2024-03-13T09:38:52.396968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
173947 45
 
0.4%
162897 38
 
0.3%
131733 34
 
0.3%
175986 30
 
0.3%
181360 28
 
0.3%
144080 26
 
0.2%
182520 25
 
0.2%
170344 18
 
0.2%
198626 18
 
0.2%
180000 18
 
0.2%
Other values (8254) 10841
97.5%
ValueCountFrequency (%)
17826 1
< 0.1%
23506 1
< 0.1%
24000 1
< 0.1%
31106 1
< 0.1%
31824 1
< 0.1%
32986 1
< 0.1%
32996 1
< 0.1%
36006 1
< 0.1%
36453 1
< 0.1%
37680 1
< 0.1%
ValueCountFrequency (%)
4120258 1
< 0.1%
3600014 1
< 0.1%
3340672 1
< 0.1%
2959346 1
< 0.1%
2733257 1
< 0.1%
1817447 1
< 0.1%
1798933 1
< 0.1%
1755311 2
< 0.1%
1736852 1
< 0.1%
1613160 1
< 0.1%

explicit
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
False
10042 
True
1079 
ValueCountFrequency (%)
False 10042
90.3%
True 1079
 
9.7%
2024-03-13T09:38:52.786099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

danceability
Real number (ℝ)

Distinct914
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59246235
Minimum0
Maximum0.974
Zeros81
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size87.0 KiB
2024-03-13T09:38:53.031552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.213
Q10.504
median0.616
Q30.716
95-th percentile0.835
Maximum0.974
Range0.974
Interquartile range (IQR)0.212

Descriptive statistics

Standard deviation0.1787108
Coefficient of variation (CV)0.30164077
Kurtosis0.77680355
Mean0.59246235
Median Absolute Deviation (MAD)0.105
Skewness-0.87027896
Sum6588.7738
Variance0.031937549
MonotonicityNot monotonic
2024-03-13T09:38:53.327077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
 
0.7%
0.647 70
 
0.6%
0.579 62
 
0.6%
0.71 61
 
0.5%
0.691 61
 
0.5%
0.524 56
 
0.5%
0.582 53
 
0.5%
0.813 52
 
0.5%
0.596 49
 
0.4%
0.593 49
 
0.4%
Other values (904) 10527
94.7%
ValueCountFrequency (%)
0 81
0.7%
0.0548 1
 
< 0.1%
0.055 1
 
< 0.1%
0.0555 1
 
< 0.1%
0.0558 1
 
< 0.1%
0.0578 1
 
< 0.1%
0.0601 1
 
< 0.1%
0.0602 1
 
< 0.1%
0.0611 1
 
< 0.1%
0.0625 1
 
< 0.1%
ValueCountFrequency (%)
0.974 1
< 0.1%
0.965 1
< 0.1%
0.963 1
< 0.1%
0.96 1
< 0.1%
0.959 1
< 0.1%
0.957 1
< 0.1%
0.956 1
< 0.1%
0.955 1
< 0.1%
0.952 1
< 0.1%
0.95 2
< 0.1%

energy
Real number (ℝ)

HIGH CORRELATION 

Distinct1283
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60008693
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size87.0 KiB
2024-03-13T09:38:53.632125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.11
Q10.427
median0.632
Q30.808
95-th percentile0.952
Maximum1
Range1
Interquartile range (IQR)0.381

Descriptive statistics

Standard deviation0.25451765
Coefficient of variation (CV)0.42413463
Kurtosis-0.58575814
Mean0.60008693
Median Absolute Deviation (MAD)0.189
Skewness-0.49138125
Sum6673.5667
Variance0.064779232
MonotonicityNot monotonic
2024-03-13T09:38:53.942776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.563 57
 
0.5%
0.876 51
 
0.5%
0.776 42
 
0.4%
0.608 40
 
0.4%
0.502 38
 
0.3%
0.287 38
 
0.3%
0.705 36
 
0.3%
0.415 35
 
0.3%
0.739 34
 
0.3%
0.75 32
 
0.3%
Other values (1273) 10718
96.4%
ValueCountFrequency (%)
0 1
 
< 0.1%
2.01 × 10-510
0.1%
2.02 × 10-51
 
< 0.1%
2.03 × 10-517
0.2%
3.05 × 10-51
 
< 0.1%
4.28 × 10-52
 
< 0.1%
5.9 × 10-51
 
< 0.1%
6.03 × 10-51
 
< 0.1%
6.19 × 10-53
 
< 0.1%
7.99 × 10-51
 
< 0.1%
ValueCountFrequency (%)
1 9
 
0.1%
0.999 13
0.1%
0.998 28
0.3%
0.997 29
0.3%
0.996 18
0.2%
0.995 21
0.2%
0.994 14
0.1%
0.993 9
 
0.1%
0.992 5
 
< 0.1%
0.991 3
 
< 0.1%

key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2098732
Minimum0
Maximum11
Zeros1317
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size87.0 KiB
2024-03-13T09:38:54.202844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5625217
Coefficient of variation (CV)0.68380199
Kurtosis-1.2798167
Mean5.2098732
Median Absolute Deviation (MAD)3
Skewness0.022488817
Sum57939
Variance12.691561
MonotonicityNot monotonic
2024-03-13T09:38:54.427512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 1317
11.8%
7 1218
11.0%
1 1167
10.5%
2 1084
9.7%
5 994
8.9%
9 936
8.4%
11 851
7.7%
8 819
7.4%
6 798
7.2%
4 775
7.0%
Other values (2) 1162
10.4%
ValueCountFrequency (%)
0 1317
11.8%
1 1167
10.5%
2 1084
9.7%
3 391
 
3.5%
4 775
7.0%
5 994
8.9%
6 798
7.2%
7 1218
11.0%
8 819
7.4%
9 936
8.4%
ValueCountFrequency (%)
11 851
7.7%
10 771
6.9%
9 936
8.4%
8 819
7.4%
7 1218
11.0%
6 798
7.2%
5 994
8.9%
4 775
7.0%
3 391
 
3.5%
2 1084
9.7%

loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct6861
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.169955
Minimum-49.307
Maximum0.082
Zeros0
Zeros (%)0.0%
Negative11119
Negative (%)> 99.9%
Memory size87.0 KiB
2024-03-13T09:38:54.691532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-49.307
5-th percentile-21.769
Q1-10.747
median-7.596
Q3-5.472
95-th percentile-3.147
Maximum0.082
Range49.389
Interquartile range (IQR)5.275

Descriptive statistics

Standard deviation6.0177163
Coefficient of variation (CV)-0.65624273
Kurtosis6.0218299
Mean-9.169955
Median Absolute Deviation (MAD)2.463
Skewness-2.1861464
Sum-101979.07
Variance36.21291
MonotonicityNot monotonic
2024-03-13T09:38:55.005659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.135 44
 
0.4%
-5.662 41
 
0.4%
-7.57 36
 
0.3%
-12.472 31
 
0.3%
-4.177 28
 
0.3%
-8.169 27
 
0.2%
-6.264 26
 
0.2%
-4.67 20
 
0.2%
-5.926 19
 
0.2%
-4.457 19
 
0.2%
Other values (6851) 10830
97.4%
ValueCountFrequency (%)
-49.307 1
 
< 0.1%
-46.251 1
 
< 0.1%
-43.504 1
 
< 0.1%
-43.046 3
< 0.1%
-42.995 1
 
< 0.1%
-42.631 1
 
< 0.1%
-41.739 1
 
< 0.1%
-41.531 1
 
< 0.1%
-40.843 1
 
< 0.1%
-40.795 1
 
< 0.1%
ValueCountFrequency (%)
0.082 1
< 0.1%
0.025 1
< 0.1%
-0.079 1
< 0.1%
-0.108 1
< 0.1%
-0.155 1
< 0.1%
-0.173 1
< 0.1%
-0.488 1
< 0.1%
-0.5 1
< 0.1%
-0.562 1
< 0.1%
-0.564 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
1
7257 
0
3864 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11121
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 7257
65.3%
0 3864
34.7%

Length

2024-03-13T09:38:55.279023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:38:55.521080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 7257
65.3%
0 3864
34.7%

Most occurring characters

ValueCountFrequency (%)
1 7257
65.3%
0 3864
34.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7257
65.3%
0 3864
34.7%

Most occurring scripts

ValueCountFrequency (%)
Common 11121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7257
65.3%
0 3864
34.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7257
65.3%
0 3864
34.7%

speechiness
Real number (ℝ)

Distinct1239
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10750614
Minimum0
Maximum0.965
Zeros81
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size87.0 KiB
2024-03-13T09:38:55.757871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0279
Q10.0361
median0.0498
Q30.0911
95-th percentile0.36
Maximum0.965
Range0.965
Interquartile range (IQR)0.055

Descriptive statistics

Standard deviation0.17184471
Coefficient of variation (CV)1.5984641
Kurtosis14.748142
Mean0.10750614
Median Absolute Deviation (MAD)0.0174
Skewness3.8094159
Sum1195.5758
Variance0.029530605
MonotonicityNot monotonic
2024-03-13T09:38:56.074638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
 
0.7%
0.102 73
 
0.7%
0.0341 67
 
0.6%
0.0323 55
 
0.5%
0.0324 51
 
0.5%
0.185 50
 
0.4%
0.0363 45
 
0.4%
0.0513 45
 
0.4%
0.0311 44
 
0.4%
0.0336 43
 
0.4%
Other values (1229) 10567
95.0%
ValueCountFrequency (%)
0 81
0.7%
0.0222 1
 
< 0.1%
0.0231 4
 
< 0.1%
0.0233 2
 
< 0.1%
0.0235 4
 
< 0.1%
0.0236 2
 
< 0.1%
0.0237 1
 
< 0.1%
0.0238 1
 
< 0.1%
0.0239 3
 
< 0.1%
0.024 4
 
< 0.1%
ValueCountFrequency (%)
0.965 1
 
< 0.1%
0.963 1
 
< 0.1%
0.962 3
< 0.1%
0.961 1
 
< 0.1%
0.96 1
 
< 0.1%
0.959 2
 
< 0.1%
0.958 5
< 0.1%
0.957 3
< 0.1%
0.956 3
< 0.1%
0.955 6
0.1%

acousticness
Real number (ℝ)

HIGH CORRELATION 

Distinct2429
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39411673
Minimum0
Maximum0.996
Zeros14
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size87.0 KiB
2024-03-13T09:38:56.370692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00177
Q10.0635
median0.307
Q30.73
95-th percentile0.959
Maximum0.996
Range0.996
Interquartile range (IQR)0.6665

Descriptive statistics

Standard deviation0.33947942
Coefficient of variation (CV)0.8613677
Kurtosis-1.3672666
Mean0.39411673
Median Absolute Deviation (MAD)0.282
Skewness0.3683011
Sum4382.9722
Variance0.11524628
MonotonicityNot monotonic
2024-03-13T09:38:56.671610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.733 47
 
0.4%
0.881 46
 
0.4%
0.764 46
 
0.4%
0.994 45
 
0.4%
0.0254 45
 
0.4%
0.29 42
 
0.4%
0.777 42
 
0.4%
0.995 42
 
0.4%
0.993 36
 
0.3%
0.458 32
 
0.3%
Other values (2419) 10698
96.2%
ValueCountFrequency (%)
0 14
0.1%
1.01 × 10-61
 
< 0.1%
1.14 × 10-61
 
< 0.1%
1.17 × 10-61
 
< 0.1%
1.21 × 10-61
 
< 0.1%
1.46 × 10-62
 
< 0.1%
1.62 × 10-62
 
< 0.1%
1.91 × 10-61
 
< 0.1%
2.26 × 10-61
 
< 0.1%
2.65 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.996 11
 
0.1%
0.995 42
0.4%
0.994 45
0.4%
0.993 36
0.3%
0.992 21
0.2%
0.991 27
0.2%
0.99 24
0.2%
0.989 18
 
0.2%
0.988 14
 
0.1%
0.987 16
 
0.1%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct3025
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13705639
Minimum0
Maximum1
Zeros4061
Zeros (%)36.5%
Negative0
Negative (%)0.0%
Memory size87.0 KiB
2024-03-13T09:38:56.972695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.02 × 10-5
Q30.0141
95-th percentile0.897
Maximum1
Range1
Interquartile range (IQR)0.0141

Descriptive statistics

Standard deviation0.29612687
Coefficient of variation (CV)2.1606207
Kurtosis2.1094291
Mean0.13705639
Median Absolute Deviation (MAD)2.02 × 10-5
Skewness1.955798
Sum1524.2041
Variance0.087691126
MonotonicityNot monotonic
2024-03-13T09:38:57.281260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4061
36.5%
9.71 × 10-644
 
0.4%
3.59 × 10-541
 
0.4%
1.68 × 10-533
 
0.3%
1.38 × 10-630
 
0.3%
1.81 × 10-618
 
0.2%
1.32 × 10-517
 
0.2%
3.87 × 10-516
 
0.1%
0.895 15
 
0.1%
1.1 × 10-515
 
0.1%
Other values (3015) 6831
61.4%
ValueCountFrequency (%)
0 4061
36.5%
1 × 10-62
 
< 0.1%
1.01 × 10-67
 
0.1%
1.02 × 10-64
 
< 0.1%
1.03 × 10-64
 
< 0.1%
1.04 × 10-64
 
< 0.1%
1.05 × 10-63
 
< 0.1%
1.06 × 10-63
 
< 0.1%
1.07 × 10-68
 
0.1%
1.08 × 10-62
 
< 0.1%
ValueCountFrequency (%)
1 5
< 0.1%
0.999 10
0.1%
0.998 3
 
< 0.1%
0.997 5
< 0.1%
0.996 2
 
< 0.1%
0.995 8
0.1%
0.994 2
 
< 0.1%
0.993 4
 
< 0.1%
0.992 2
 
< 0.1%
0.991 7
0.1%

liveness
Real number (ℝ)

Distinct1429
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21439206
Minimum0
Maximum0.994
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size87.0 KiB
2024-03-13T09:38:57.578693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0604
Q10.0973
median0.128
Q30.262
95-th percentile0.715
Maximum0.994
Range0.994
Interquartile range (IQR)0.1647

Descriptive statistics

Standard deviation0.19877875
Coefficient of variation (CV)0.927174
Kurtosis3.9598438
Mean0.21439206
Median Absolute Deviation (MAD)0.046
Skewness2.0878633
Sum2384.2541
Variance0.039512991
MonotonicityNot monotonic
2024-03-13T09:38:58.331935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11 154
 
1.4%
0.108 148
 
1.3%
0.111 140
 
1.3%
0.109 139
 
1.2%
0.112 131
 
1.2%
0.113 131
 
1.2%
0.107 127
 
1.1%
0.104 117
 
1.1%
0.106 101
 
0.9%
0.103 99
 
0.9%
Other values (1419) 9834
88.4%
ValueCountFrequency (%)
0 1
< 0.1%
0.00925 1
< 0.1%
0.0137 1
< 0.1%
0.0151 1
< 0.1%
0.0153 1
< 0.1%
0.017 1
< 0.1%
0.0173 1
< 0.1%
0.0188 2
< 0.1%
0.0197 1
< 0.1%
0.0202 1
< 0.1%
ValueCountFrequency (%)
0.994 1
 
< 0.1%
0.992 1
 
< 0.1%
0.99 1
 
< 0.1%
0.989 1
 
< 0.1%
0.984 1
 
< 0.1%
0.983 2
< 0.1%
0.981 4
< 0.1%
0.98 1
 
< 0.1%
0.979 2
< 0.1%
0.977 1
 
< 0.1%

valence
Real number (ℝ)

Distinct1348
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48992505
Minimum0
Maximum0.992
Zeros90
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size87.0 KiB
2024-03-13T09:38:58.619462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0535
Q10.288
median0.49
Q30.699
95-th percentile0.917
Maximum0.992
Range0.992
Interquartile range (IQR)0.411

Descriptive statistics

Standard deviation0.26121491
Coefficient of variation (CV)0.5331732
Kurtosis-0.97655548
Mean0.48992505
Median Absolute Deviation (MAD)0.206
Skewness-0.023738117
Sum5448.4565
Variance0.068233227
MonotonicityNot monotonic
2024-03-13T09:38:58.927153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 90
 
0.8%
1 × 10-567
 
0.6%
0.961 62
 
0.6%
0.324 61
 
0.5%
0.64 55
 
0.5%
0.949 47
 
0.4%
0.639 42
 
0.4%
0.836 40
 
0.4%
0.56 37
 
0.3%
0.628 36
 
0.3%
Other values (1338) 10584
95.2%
ValueCountFrequency (%)
0 90
0.8%
1 × 10-567
0.6%
0.000322 1
 
< 0.1%
0.000378 1
 
< 0.1%
0.000667 1
 
< 0.1%
0.000673 1
 
< 0.1%
0.000755 1
 
< 0.1%
0.000781 1
 
< 0.1%
0.00084 1
 
< 0.1%
0.000885 1
 
< 0.1%
ValueCountFrequency (%)
0.992 1
 
< 0.1%
0.985 1
 
< 0.1%
0.983 1
 
< 0.1%
0.98 3
< 0.1%
0.979 2
 
< 0.1%
0.978 3
< 0.1%
0.977 6
0.1%
0.975 2
 
< 0.1%
0.973 4
< 0.1%
0.972 6
0.1%

tempo
Real number (ℝ)

HIGH CORRELATION 

Distinct7909
Distinct (%)71.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.29995
Minimum0
Maximum220.081
Zeros81
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size87.0 KiB
2024-03-13T09:38:59.245264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75.157
Q194.275
median116.03
Q3135.039
95-th percentile171.698
Maximum220.081
Range220.081
Interquartile range (IQR)40.764

Descriptive statistics

Standard deviation30.148845
Coefficient of variation (CV)0.25923353
Kurtosis0.86364119
Mean116.29995
Median Absolute Deviation (MAD)20.826
Skewness-0.0092707951
Sum1293371.7
Variance908.95287
MonotonicityNot monotonic
2024-03-13T09:38:59.534454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
 
0.7%
106.998 45
 
0.4%
151.925 38
 
0.3%
76.783 34
 
0.3%
77.117 30
 
0.3%
128.89 27
 
0.2%
146.861 26
 
0.2%
137.494 25
 
0.2%
91.921 19
 
0.2%
119.935 18
 
0.2%
Other values (7899) 10778
96.9%
ValueCountFrequency (%)
0 81
0.7%
30.2 1
 
< 0.1%
35.392 1
 
< 0.1%
43.844 1
 
< 0.1%
44.153 1
 
< 0.1%
45.664 1
 
< 0.1%
45.857 1
 
< 0.1%
46.496 1
 
< 0.1%
46.592 1
 
< 0.1%
47.604 1
 
< 0.1%
ValueCountFrequency (%)
220.081 3
< 0.1%
220.039 1
 
< 0.1%
219.693 1
 
< 0.1%
213.848 1
 
< 0.1%
213.778 1
 
< 0.1%
208.038 2
< 0.1%
208.001 1
 
< 0.1%
207.348 1
 
< 0.1%
207.266 1
 
< 0.1%
204.961 1
 
< 0.1%

time_signature
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
4
9668 
3
1008 
5
 
205
1
 
159
0
 
81

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11121
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 9668
86.9%
3 1008
 
9.1%
5 205
 
1.8%
1 159
 
1.4%
0 81
 
0.7%

Length

2024-03-13T09:38:59.792056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:39:00.055660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4 9668
86.9%
3 1008
 
9.1%
5 205
 
1.8%
1 159
 
1.4%
0 81
 
0.7%

Most occurring characters

ValueCountFrequency (%)
4 9668
86.9%
3 1008
 
9.1%
5 205
 
1.8%
1 159
 
1.4%
0 81
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11121
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 9668
86.9%
3 1008
 
9.1%
5 205
 
1.8%
1 159
 
1.4%
0 81
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 11121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 9668
86.9%
3 1008
 
9.1%
5 205
 
1.8%
1 159
 
1.4%
0 81
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 9668
86.9%
3 1008
 
9.1%
5 205
 
1.8%
1 159
 
1.4%
0 81
 
0.7%

track_genre
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size87.0 KiB
indian
999 
pop-film
889 
afrobeat
782 
k-pop
 
697
sleep
 
614
Other values (14)
7140 

Length

Max length9
Median length8
Mean length6.0356083
Min length3

Characters and Unicode

Total characters67122
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowacoustic
2nd rowacoustic
3rd rowacoustic
4th rowacoustic
5th rowacoustic

Common Values

ValueCountFrequency (%)
indian 999
 
9.0%
pop-film 889
 
8.0%
afrobeat 782
 
7.0%
k-pop 697
 
6.3%
sleep 614
 
5.5%
breakbeat 591
 
5.3%
acoustic 589
 
5.3%
blues 583
 
5.2%
chill 560
 
5.0%
disco 524
 
4.7%
Other values (9) 4293
38.6%

Length

2024-03-13T09:39:00.304168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
indian 999
 
9.0%
pop-film 889
 
8.0%
afrobeat 782
 
7.0%
k-pop 697
 
6.3%
sleep 614
 
5.5%
breakbeat 591
 
5.3%
acoustic 589
 
5.3%
blues 583
 
5.2%
chill 560
 
5.0%
disco 524
 
4.7%
Other values (9) 4293
38.6%

Most occurring characters

ValueCountFrequency (%)
a 6566
 
9.8%
l 5694
 
8.5%
i 5557
 
8.3%
o 5515
 
8.2%
p 5308
 
7.9%
e 4765
 
7.1%
c 4731
 
7.0%
s 4088
 
6.1%
b 3058
 
4.6%
d 2803
 
4.2%
Other values (10) 19037
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 65023
96.9%
Dash Punctuation 2099
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6566
 
10.1%
l 5694
 
8.8%
i 5557
 
8.5%
o 5515
 
8.5%
p 5308
 
8.2%
e 4765
 
7.3%
c 4731
 
7.3%
s 4088
 
6.3%
b 3058
 
4.7%
d 2803
 
4.3%
Other values (9) 16938
26.0%
Dash Punctuation
ValueCountFrequency (%)
- 2099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 65023
96.9%
Common 2099
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6566
 
10.1%
l 5694
 
8.8%
i 5557
 
8.5%
o 5515
 
8.5%
p 5308
 
8.2%
e 4765
 
7.3%
c 4731
 
7.3%
s 4088
 
6.3%
b 3058
 
4.7%
d 2803
 
4.3%
Other values (9) 16938
26.0%
Common
ValueCountFrequency (%)
- 2099
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67122
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6566
 
9.8%
l 5694
 
8.5%
i 5557
 
8.3%
o 5515
 
8.2%
p 5308
 
7.9%
e 4765
 
7.1%
c 4731
 
7.0%
s 4088
 
6.1%
b 3058
 
4.6%
d 2803
 
4.2%
Other values (10) 19037
28.4%

Interactions

2024-03-13T09:38:45.424938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:05.090383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:08.026643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:12.040882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:16.842979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:20.285222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:23.419390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:28.941693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:32.005652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:34.999736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:38.581785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:42.021595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:45.825085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:05.330352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:08.398541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:12.435965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:17.165632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:20.623578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:23.677588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:29.184874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:32.247590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:35.265093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:38.877576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:42.263666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:46.201368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:05.561880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:08.632316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:12.863063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:17.525656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:20.881564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:23.923238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:29.435435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:32.504354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:35.523454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:39.174224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:42.882246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:46.601470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:05.809097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:08.963965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:13.479513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:17.812252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:21.167069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:24.354366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:29.682131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:32.744473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:35.768051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:39.517033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:43.118684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:46.917799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:06.063870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:09.212756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:14.088886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:18.185590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:21.406586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:24.810390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:29.950741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:32.985311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:36.011832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:39.903630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:43.368219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:47.285649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:06.307457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:09.463702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:14.354182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:18.552445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:21.658662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:25.271614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:30.194314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:33.229507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:36.274948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:40.133578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:43.625335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:47.701935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:06.542094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:09.710747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:14.663650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:18.799097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:21.909971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:25.779351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:30.448475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:33.483406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:36.541438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:40.452234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:43.873186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:48.047039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:06.783153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:10.062635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:15.039738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:19.069808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:22.155931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:26.421361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:30.689191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:33.717479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:36.784825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:40.702488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:44.119360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:48.329221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:07.042506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:10.320271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:15.434914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:19.329249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:22.408035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:26.911967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:30.937492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:33.959977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:37.082388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:40.945207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:44.366668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:48.577501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:07.282907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:10.689355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:15.814329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:19.590333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:22.673186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:27.202475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:31.257033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:34.224414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:37.426347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:41.193419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:44.638780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:48.837066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:07.537341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:11.077511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:16.169184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:19.826631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:22.925013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:27.527103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:31.503268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:34.466067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:37.808779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:41.460757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:44.899873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:49.072720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:07.774609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:11.543369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:16.471112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:20.063365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:23.174849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:27.779174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:31.755081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:34.711752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:38.180881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:41.770609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T09:38:45.154198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-03-13T09:39:00.542532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
acousticnessdanceabilityduration_msenergyexplicitinstrumentalnesskeylivenessloudnessmodepopularityspeechinesstempotime_signaturetrack_genrevalence
acousticness1.000-0.347-0.091-0.6230.1110.032-0.0420.014-0.5670.1110.011-0.141-0.2240.1490.290-0.255
danceability-0.3471.000-0.0700.2590.215-0.1670.048-0.1460.3490.0760.0660.2250.1100.3800.3090.496
duration_ms-0.091-0.0701.0000.1520.0000.0530.015-0.0460.0740.0180.089-0.1210.0640.0520.0750.038
energy-0.6230.2590.1521.0000.129-0.1110.0500.1710.7270.102-0.0700.3330.2150.1900.3020.391
explicit0.1110.2150.0000.1291.000-0.1820.0000.1280.0280.000-0.0130.315-0.0360.0680.548-0.029
instrumentalness0.032-0.1670.053-0.111-0.1821.000-0.002-0.108-0.3220.018-0.085-0.159-0.0250.1230.247-0.262
key-0.0420.0480.0150.0500.000-0.0021.0000.0170.0340.2450.0040.0640.0200.0320.0660.059
liveness0.014-0.146-0.0460.1710.128-0.1080.0171.0000.0340.027-0.0830.156-0.0200.1070.210-0.022
loudness-0.5670.3490.0740.7270.028-0.3220.0340.0341.0000.0670.0320.1680.1980.1940.3010.375
mode0.1110.0760.0180.1020.0000.0180.2450.0270.0671.000-0.086-0.076-0.0250.0270.205-0.026
popularity0.0110.0660.089-0.070-0.013-0.0850.004-0.0830.032-0.0861.000-0.036-0.0020.1080.455-0.064
speechiness-0.1410.225-0.1210.3330.315-0.1590.0640.1560.168-0.076-0.0361.0000.1160.1190.3040.153
tempo-0.2240.1100.0640.215-0.036-0.0250.020-0.0200.198-0.025-0.0020.1161.0000.5080.1850.159
time_signature0.1490.3800.0520.1900.0680.1230.0320.1070.1940.0270.1080.1190.5081.0000.2500.168
track_genre0.2900.3090.0750.3020.5480.2470.0660.2100.3010.2050.4550.3040.1850.2501.000-0.097
valence-0.2550.4960.0380.391-0.029-0.2620.059-0.0220.375-0.026-0.0640.1530.1590.168-0.0971.000

Missing values

2024-03-13T09:38:49.461825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T09:38:50.185121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

artistsalbum_nametrack_namepopularityduration_msexplicitdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signaturetrack_genre
0Gen HoshinoComedyComedy73230666False0.6760.46101-6.74600.14300.03220.0000010.35800.715087.9174acoustic
1Ben WoodwardGhost (Acoustic)Ghost - Acoustic55149610False0.4200.16601-17.23510.07630.92400.0000060.10100.267077.4894acoustic
2Ingrid Michaelson;ZAYNTo Begin AgainTo Begin Again57210826False0.4380.35900-9.73410.05570.21000.0000000.11700.120076.3324acoustic
3Kina GrannisCrazy Rich Asians (Original Motion Picture Soundtrack)Can't Help Falling In Love71201933False0.2660.05960-18.51510.03630.90500.0000710.13200.1430181.7403acoustic
4Chord OverstreetHold OnHold On82198853False0.6180.44302-9.68110.05260.46900.0000000.08290.1670119.9494acoustic
5Tyrone WellsDays I Will RememberDays I Will Remember58214240False0.6880.48106-8.80710.10500.28900.0000000.18900.666098.0174acoustic
6A Great Big World;Christina AguileraIs There Anybody Out There?Say Something74229400False0.4070.14702-8.82210.03550.85700.0000030.09130.0765141.2843acoustic
7Jason MrazWe Sing. We Dance. We Steal Things.I'm Yours80242946False0.7030.444011-9.33110.04170.55900.0000000.09730.7120150.9604acoustic
8Jason Mraz;Colbie CaillatWe Sing. We Dance. We Steal Things.Lucky74189613False0.6250.41400-8.70010.03690.29400.0000000.15100.6690130.0884acoustic
9Ross CoppermanHungerHunger56205594False0.4420.63201-6.77010.02950.42600.0041900.07350.196078.8994acoustic
artistsalbum_nametrack_namepopularityduration_msexplicitdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signaturetrack_genre
11111Jamie MillerI Lost Myself In Loving YouI Lost Myself In Loving You67201306False0.4560.6151-5.45410.05290.614000.0000000.09080.416078.4614soul
11112Brandy;MonicaGourmet SoulThe Boy Is Mine0239683False0.6590.7261-6.01300.04150.308000.0000130.13900.709093.0444soul
11113Jessie WareAutumn Vibes 2022Mirage (Don’t Stop)0287226False0.7960.7102-6.68310.08380.109000.0008460.12400.6210112.9764soul
11114CelesteAutumn VibeTonight Tonight1219440False0.6110.83411-4.52800.02990.054400.0000160.23200.6430109.9974soul
11115Faith EvansGourmet SoulYou Used to Love Me0269173False0.7020.4580-8.19510.03480.247000.0000020.12600.679089.2664soul
11116Faith Evans;The Notorious B.I.G.Mellow Bars R'n'BOne in the Same0205706True0.4610.5401-7.38500.06290.268000.0000000.34200.1070136.5625soul
11117Brandy;MasePop StationTop of the World0281506False0.8390.6211-5.73910.13200.003010.0338000.10500.855098.0154soul
11118Jessie WareAnímate, es viernesPlease - Single Edit0219933False0.6650.6907-7.27510.03690.032500.0147000.11600.4540120.0324soul
11119CommodoresCoffee BreakThree Times A Lady1397586False0.4860.2428-12.02910.02600.809000.0003260.17700.085574.7823soul
11120CommodoresOn air 70's HitsEasy1256545False0.5680.57510-7.76900.02900.126000.0001000.11400.3330133.0934soul

Duplicate rows

Most frequently occurring

popularityduration_msexplicitdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signaturetrack_genre# duplicates
670162897False0.6470.87610-5.66210.18500.88100.0000360.26000.949151.9254blues36
140131733False0.5790.5028-7.57010.05130.73300.0000000.28100.83676.7834blues32
1060175986False0.5930.2871-12.47210.04690.76400.0000000.15300.63977.1174blues29
300144080False0.6910.7760-4.17710.03410.45800.0000170.33000.961146.8614soul26
1230181360False0.5820.7053-8.16910.05390.29000.0000010.79700.640128.8904soul25
1270182520False0.7750.6087-6.26410.12800.12800.0000000.08950.560137.4944dance23
1000173947True0.9050.5638-6.13510.10200.02540.0000100.11300.324106.9984hip-hop20
1010173947True0.9050.5638-6.13510.10200.02540.0000100.11300.324106.9984k-pop20
1850198626False0.5240.7390-6.68110.03860.21500.0000000.06360.48083.0954soul17
940171773False0.5960.3158-9.17510.04280.96100.0000000.25800.640119.9354blues16